Face detection algorithms can be broadly classified into four categories, namely, knowledge-based methods, feature-invariant approaches, template matching methods, and appearance-based methods. A knowledge-based method is a top down approach to face detection, where human knowledge of facial features is coded as rules that define a face. A feature-invariant approach is a bottom up approach to face detection, which is based on the understanding that face regions contain a set of illumination and pose invariant features that can be computed. These features are local features such as edges and average intensities of regions within a face. The relation between local features is also exploited for face detection. In a template matching method, several templates covering possible variations of faces are stored and correlations between the input image and the templates are computed. An image is classified as either face or non face based on a measure of deviation of the input image from the templates. In an appearance-based method, models for a face are learned from a training set and then used to detect faces. Each model is expected to incorporate the possible variations in face shapes and illuminations.
In any of the above approaches, face detection in a still image involves searching for a face sub image or sub images within the space of an input image when the image contains more than one face. Since the search space is very large, the face detection algorithm is required to have low complexity if the face detector is employed on live videos. The appearance-based approaches can deliver the accuracy required but at a high computation cost and complexity. Algorithms such as feature invariant approaches have low complexity but may not deliver the desired accuracy in certain applications.
Hence, there is a need for a computationally efficient hybrid computer implemented method and system for detecting interest sections, for example, face sections, in a still image.